Protein-protein interaction site prediction method based on deep map convolutional network

An interaction site, convolutional network technology, applied in neural learning methods, biological neural network models, instruments, etc., can solve problems such as inability to extract amino acid information, inability to effectively simulate complex relationships of amino acids, and achieve the effect of improving accuracy.

Pending Publication Date: 2021-07-30
SUN YAT SEN UNIV
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Problems solved by technology

However, this method of selecting neighbors is based on a relatively arbitrary distance threshold, and cannot extract the information of amino ...

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  • Protein-protein interaction site prediction method based on deep map convolutional network
  • Protein-protein interaction site prediction method based on deep map convolutional network
  • Protein-protein interaction site prediction method based on deep map convolutional network

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Embodiment 1

[0048] like figure 1 As shown, a protein-protein interaction site prediction method based on a deep graph convolutional network, the method includes the following steps:

[0049] S1: According to the sequence and structural information of the protein, extract the node feature matrix and the adjacency matrix containing edge information to form a protein graph representation together;

[0050]S2: Use deep graph convolution based on initial residual and identity mapping to capture the characteristics of high-order spatially adjacent amino acids; and input a multi-layer perceptron at the output of the last graph convolution layer of deep graph convolution, Realize the final prediction of the protein interaction probability of each amino acid, and complete the construction of a deep graph convolutional neural network;

[0051] S3: extract the training data through step S1 to obtain the protein graph representation, and use the five-fold cross-validation method to train the deep gr...

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Abstract

The invention discloses a protein-protein interaction site prediction method based on a deep map convolutional network; the method comprises the following steps: extracting a node feature matrix and an adjacent matrix containing side information according to the sequence and structure information of protein, and forming protein map representation together; carrying out deep map convolution based on an initial residual error and identical mapping; inputting the output of the last layer of image convolution layer of the deep image convolution into a multi-layer perceptron, and completing construction of the deep image convolution neural network; extracting training data to obtain protein map representation, and training the deep map convolutional neural network by adopting a five-fold cross validation method; and extracting to-be-detected data to obtain protein map representation, and inputting the protein map representation into the trained deep map convolutional neural network to realize prediction of protein-protein interaction sites. According to the method, protein space structure information can be fully utilized, and the accuracy of protein-protein interaction site prediction is further improved.

Description

technical field [0001] The present invention relates to the technical field of biological information, and more specifically, relates to a protein-protein interaction site prediction method based on a deep graph convolutional network. Background technique [0002] Protein-protein interaction (PPI) plays an important role in physiological activities such as signal transduction, substance transport and metabolism. Identifying amino acids that participate in physical contact between protein-protein complexes (ie, protein-protein interaction sites) helps to construct protein-protein interaction networks, predict protein functions, reveal disease mechanisms, and develop new drugs. However, traditional experimental methods such as two-hybrid assays and affinity purification to identify PPI sites are costly and time-consuming. Therefore, it is of great practical significance to develop calculation methods that can accurately predict PPI sites. [0003] The current calculation met...

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Application Information

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IPC IPC(8): G16B25/00G06N3/04G06N3/08
CPCG16B25/00G06N3/08G06N3/045
Inventor 杨跃东袁乾沐卢宇彤
Owner SUN YAT SEN UNIV
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